Pub Date : 2024-09-15DOI: 10.1088/2516-1091/ad776c
Yagmur Filiz, Alessio Esposito, Carmelo De Maria, Giovanni Vozzi and Ozlem Yesil-Celiktas
In the preclinical stage of drug development, 2D and 3D cell cultures under static conditions followed by animal models are utilized. However, these models are insufficient to recapitulate the complexity of human physiology. With the developing organ-on-chip (OoC) technology in recent years, human physiology and pathophysiology can be modeled better than traditional models. In this review, the need for OoC platforms is discussed and evaluated from both biological and engineering perspectives. The cellular and extracellular matrix components are discussed from a biological perspective, whereas the technical aspects such as the intricate working principles of these systems, the pivotal role played by flow dynamics and sensor integration within OoCs are elucidated from an engineering perspective. Combining these two perspectives, bioengineering applications are critically discussed with a focus on tissue barriers such as blood-brain barrier, ocular barrier, nasal barrier, pulmonary barrier and gastrointestinal barrier, featuring recent examples from the literature. Furthermore, this review offers insights into the practical utility of OoC platforms for modeling tissue barriers, showcasing their potential and drawbacks while providing future projections for innovative technologies.
{"title":"A comprehensive review on organ-on-chips as powerful preclinical models to study tissue barriers","authors":"Yagmur Filiz, Alessio Esposito, Carmelo De Maria, Giovanni Vozzi and Ozlem Yesil-Celiktas","doi":"10.1088/2516-1091/ad776c","DOIUrl":"https://doi.org/10.1088/2516-1091/ad776c","url":null,"abstract":"In the preclinical stage of drug development, 2D and 3D cell cultures under static conditions followed by animal models are utilized. However, these models are insufficient to recapitulate the complexity of human physiology. With the developing organ-on-chip (OoC) technology in recent years, human physiology and pathophysiology can be modeled better than traditional models. In this review, the need for OoC platforms is discussed and evaluated from both biological and engineering perspectives. The cellular and extracellular matrix components are discussed from a biological perspective, whereas the technical aspects such as the intricate working principles of these systems, the pivotal role played by flow dynamics and sensor integration within OoCs are elucidated from an engineering perspective. Combining these two perspectives, bioengineering applications are critically discussed with a focus on tissue barriers such as blood-brain barrier, ocular barrier, nasal barrier, pulmonary barrier and gastrointestinal barrier, featuring recent examples from the literature. Furthermore, this review offers insights into the practical utility of OoC platforms for modeling tissue barriers, showcasing their potential and drawbacks while providing future projections for innovative technologies.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-13DOI: 10.1088/2516-1091/ad6abb
Preeti Goswami, Videsh Kumar, Govind Gupta
The biomedical applications of metal dichalcogenides (MDCs) nanomaterials (NMs) are an emerging discipline because of their unique attributes like high surface-to-volume ratio, defect sites, superb catalytic performance, and excitation-dependent emission, which is helpful in bio-imaging and cancer cell killing. Due to the compatibility of sensing material with cells and tissues, MoS2, WS2, and SnS2 NMs have piqued the interest of researchers in various biomedical applications like photothermal therapy used in killing cancer cells, drug delivery, photoacoustic tomography (PAT) used in bio-imaging, nucleic acid or gene delivery, tissue engineering, wound healing, etc. Furthermore, these NMs’ functionalization and defect engineering can enhance therapeutic efficacy, biocompatibility, high drug transport efficiency, adjustable drug release, dispersibility, and biodegradability. Among the aforementioned materials, MoS2 NMs have extensively been explored via functionalization and defects engineering to improve biosensing properties. However, further enhancement is still available. Aside from MoS2, the distinct chemo-physical and optical features of WS2 and SnS2 NMs promise considerable potential in biosensing, nanomedicine, and pharmaceuticals. This article mainly focuses on the challenges and future aspects of two-dimensional MDCs NMs in biomedical applications, along with their advancements in various medical diagnosis processes.
{"title":"Biomedical prospects and challenges of metal dichalcogenides nanomaterials","authors":"Preeti Goswami, Videsh Kumar, Govind Gupta","doi":"10.1088/2516-1091/ad6abb","DOIUrl":"https://doi.org/10.1088/2516-1091/ad6abb","url":null,"abstract":"The biomedical applications of metal dichalcogenides (MDCs) nanomaterials (NMs) are an emerging discipline because of their unique attributes like high surface-to-volume ratio, defect sites, superb catalytic performance, and excitation-dependent emission, which is helpful in bio-imaging and cancer cell killing. Due to the compatibility of sensing material with cells and tissues, MoS<sub>2</sub>, WS<sub>2</sub>, and SnS<sub>2</sub> NMs have piqued the interest of researchers in various biomedical applications like photothermal therapy used in killing cancer cells, drug delivery, photoacoustic tomography (PAT) used in bio-imaging, nucleic acid or gene delivery, tissue engineering, wound healing, etc. Furthermore, these NMs’ functionalization and defect engineering can enhance therapeutic efficacy, biocompatibility, high drug transport efficiency, adjustable drug release, dispersibility, and biodegradability. Among the aforementioned materials, MoS<sub>2</sub> NMs have extensively been explored via functionalization and defects engineering to improve biosensing properties. However, further enhancement is still available. Aside from MoS<sub>2</sub>, the distinct chemo-physical and optical features of WS<sub>2</sub> and SnS<sub>2</sub> NMs promise considerable potential in biosensing, nanomedicine, and pharmaceuticals. This article mainly focuses on the challenges and future aspects of two-dimensional MDCs NMs in biomedical applications, along with their advancements in various medical diagnosis processes.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142196364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-09DOI: 10.1088/2516-1091/ad6dbf
J. Norton, James W. Martin, Conchubhair Winters, Bruno Scaglioni, K. Obstein, Venkataraman Subramanian, Pietro Valdastri
An understanding of the biological environment, and in particular the physical morphology, is crucial for those developing medical devices and software applications. It not only informs appropriate design inputs, but provides the opportunity to evaluate outputs via virtual or synthetic models before investing in costly clinical investigations. The large bowel is a pertinent example, having a major demand for effective technological solutions to clinical unmet needs. Despite numerous efforts in this area, there remains a paucity of accurate and reliable data in literature. This work reviews what is available, including both processed datasets and raw medical images, before providing a comprehensive quantitative description of the environment for biomedical engineers in this and related regions of the body. CT images from 75 patients, and a blend of different mathematical and computational methods, are used to calculate and define several crucial metrics, including: a typical adult size (abdominal girth) and abdominal shape, location (or depth) of the bowel inside the abdomen, large bowel length, lumen diameter, flexure number and characteristics, volume and anatomical tortuosity. These metrics are reviewed and defined by both gender and body posture, as well as – wherever possible – being spilt into the various anatomical regions of the large bowel. The resulting data can be used to describe a realistic “average” adult large bowel environment and so drive both design specifications and high fidelity test environments.
{"title":"The adult large bowel: describing environment morphology for effective biomedical device development","authors":"J. Norton, James W. Martin, Conchubhair Winters, Bruno Scaglioni, K. Obstein, Venkataraman Subramanian, Pietro Valdastri","doi":"10.1088/2516-1091/ad6dbf","DOIUrl":"https://doi.org/10.1088/2516-1091/ad6dbf","url":null,"abstract":"\u0000 An understanding of the biological environment, and in particular the physical morphology, is crucial for those developing medical devices and software applications. It not only informs appropriate design inputs, but provides the opportunity to evaluate outputs via virtual or synthetic models before investing in costly clinical investigations. The large bowel is a pertinent example, having a major demand for effective technological solutions to clinical unmet needs. Despite numerous efforts in this area, there remains a paucity of accurate and reliable data in literature. This work reviews what is available, including both processed datasets and raw medical images, before providing a comprehensive quantitative description of the environment for biomedical engineers in this and related regions of the body. CT images from 75 patients, and a blend of different mathematical and computational methods, are used to calculate and define several crucial metrics, including: a typical adult size (abdominal girth) and abdominal shape, location (or depth) of the bowel inside the abdomen, large bowel length, lumen diameter, flexure number and characteristics, volume and anatomical tortuosity. These metrics are reviewed and defined by both gender and body posture, as well as – wherever possible – being spilt into the various anatomical regions of the large bowel. The resulting data can be used to describe a realistic “average” adult large bowel environment and so drive both design specifications and high fidelity test environments.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141924600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-01DOI: 10.1088/2516-1091/ad5cb1
Mehdi Kazemzadeh-Narbat, Asija Memic, Kevin B McGowan, Adnan Memic and Ali Tamayol
Implant-associated infections, caused by the formation of biofilms especially antibiotic resistant organisms, are among the leading causes of orthopaedic implant failure. Current strategies to combat infection and biofilm focus on either inhibiting bacterial growth or preventing bacterial adherence that could lead to biofilm creation. Despite research on developing numerous antimicrobial orthopaedic devices, to date, no robust solution has been translated to the clinic. One of the key bottlenecks is the disconnect between researchers and regulatory agencies. In this review, we outline recent strategies for minimizing orthopaedic implant-associated infections. In addition, we discuss the relevant Food and Drug Administration regulatory perspectives, challenges. We also highlight emerging technologies and the directions the field that is expected to expand. We discuss in depth challenges that include identifying strategies that render implants antibacterial permanently or for a long period of time without the use of antimicrobial compounds that could generate resistance in pathogens and negatively impact osseointegration.
{"title":"Advances in antimicrobial orthopaedic devices and FDA regulatory challenges","authors":"Mehdi Kazemzadeh-Narbat, Asija Memic, Kevin B McGowan, Adnan Memic and Ali Tamayol","doi":"10.1088/2516-1091/ad5cb1","DOIUrl":"https://doi.org/10.1088/2516-1091/ad5cb1","url":null,"abstract":"Implant-associated infections, caused by the formation of biofilms especially antibiotic resistant organisms, are among the leading causes of orthopaedic implant failure. Current strategies to combat infection and biofilm focus on either inhibiting bacterial growth or preventing bacterial adherence that could lead to biofilm creation. Despite research on developing numerous antimicrobial orthopaedic devices, to date, no robust solution has been translated to the clinic. One of the key bottlenecks is the disconnect between researchers and regulatory agencies. In this review, we outline recent strategies for minimizing orthopaedic implant-associated infections. In addition, we discuss the relevant Food and Drug Administration regulatory perspectives, challenges. We also highlight emerging technologies and the directions the field that is expected to expand. We discuss in depth challenges that include identifying strategies that render implants antibacterial permanently or for a long period of time without the use of antimicrobial compounds that could generate resistance in pathogens and negatively impact osseointegration.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141886703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting. 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyse (PRISMA) guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). To solve the small data issue transfer learning technique, basic data augmentation and generative adversarial network were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only four studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items). The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
{"title":"Tackling the small data problem in medical image classification with artificial intelligence: a systematic review","authors":"Stefano Piffer, Leonardo Ubaldi, Sabina Tangaro, Alessandra Retico and Cinzia Talamonti","doi":"10.1088/2516-1091/ad525b","DOIUrl":"https://doi.org/10.1088/2516-1091/ad525b","url":null,"abstract":"Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting. 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the Preferred Reporting Items for Systematic reviews and Meta-Analyse (PRISMA) guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). To solve the small data issue transfer learning technique, basic data augmentation and generative adversarial network were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only four studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items). The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141510631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-03DOI: 10.1088/2516-1091/ad3a4b
N. Sriraam, Babu Chinta, S. Suresh, Suresh Sudarshan
For prenatal screening, ultrasound imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as AI-based ultrasound image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from ultrasound video feeds. AI modeling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.
{"title":"Ultrasound Imaging based recognition of prenatal anomalies: A systematic clinical engineeringreview","authors":"N. Sriraam, Babu Chinta, S. Suresh, Suresh Sudarshan","doi":"10.1088/2516-1091/ad3a4b","DOIUrl":"https://doi.org/10.1088/2516-1091/ad3a4b","url":null,"abstract":"\u0000 For prenatal screening, ultrasound imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as AI-based ultrasound image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from ultrasound video feeds. AI modeling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140747349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.
{"title":"Trends and advances in silk based 3D printing/bioprinting towards cartilage tissue engineering and regeneration","authors":"Yogendra Pratap Singh, Ashutosh Bandyopadhyay, Souradeep Dey, Nandana Bhardwaj, Biman B Mandal","doi":"10.1088/2516-1091/ad2d59","DOIUrl":"https://doi.org/10.1088/2516-1091/ad2d59","url":null,"abstract":"Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140315791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-24DOI: 10.1088/2516-1091/ad225a
A. M. Ponsiglione, P. Zaffino, C. Ricciardi, Danilo Di Laura, M. Spadea, Gianmaria De Tommasi, G. Improta, Maria Romano, Francesco Amato
Simulation models and artificial intelligence are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and artificial intelligence could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and artificial intelligence approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and artificial intelligence as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed artificial intelligence strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.
仿真模型和人工智能在很大程度上被用于解决医疗保健和生物医学工程问题。这两种方法在分析和优化医疗保健流程方面都取得了可喜的成果。因此，仿真模型与人工智能的结合可以为进一步提高医疗服务质量提供一种策略。在这项工作中，我们对应用仿真模型和人工智能混合方法应对医疗保健管理挑战的研究进行了系统综述。独立审稿人对 Scopus、Web of Science 和 PubMed 数据库进行了筛选。确定并讨论了仿真与人工智能相结合的主要策略以及主要的医疗应用场景。此外，还介绍了实施建议方法的工具和算法。结果表明，机器学习似乎是与仿真模型结合使用最多的人工智能策略，而仿真模型主要依赖于基于代理的系统和离散事件系统。所纳入研究的稀缺性和异质性表明，在医疗保健管理中实施机器学习-模拟混合方法的标准化框架尚未确定。未来的工作目标应该是利用这些方法为医疗保健流程设计新颖的智能内嵌模型，并将其有效地应用于临床。
{"title":"Combining simulation models and machine learning in healthcare management: Strategies and applications","authors":"A. M. Ponsiglione, P. Zaffino, C. Ricciardi, Danilo Di Laura, M. Spadea, Gianmaria De Tommasi, G. Improta, Maria Romano, Francesco Amato","doi":"10.1088/2516-1091/ad225a","DOIUrl":"https://doi.org/10.1088/2516-1091/ad225a","url":null,"abstract":"\u0000 Simulation models and artificial intelligence are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and artificial intelligence could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and artificial intelligence approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and artificial intelligence as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed artificial intelligence strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139602661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-09DOI: 10.1088/2516-1091/ad1cd6
F. Reffuveille, Yasser Dghoughi, M. Colin, M. Torres, C. de la Fuente-Nunez
The lack of effective antibiotics for drug-resistant infections has led the World Health Organization (WHO) to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.
{"title":"Antibiofilm approaches as a new paradigm for treating infections","authors":"F. Reffuveille, Yasser Dghoughi, M. Colin, M. Torres, C. de la Fuente-Nunez","doi":"10.1088/2516-1091/ad1cd6","DOIUrl":"https://doi.org/10.1088/2516-1091/ad1cd6","url":null,"abstract":"\u0000 The lack of effective antibiotics for drug-resistant infections has led the World Health Organization (WHO) to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.","PeriodicalId":501097,"journal":{"name":"Progress in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-14DOI: 10.1088/2516-1091/ad10b4
Nandana Bhardwaj, Souradeep Dey, Bibrita Bhar, Biman B Mandal
In the past decade, the use of three-dimensional (3D) bioprinting technology for the development of in vitro tissue models has attracted a great deal of attention. This is due to its remarkable precision in constructing different functional tissues and organs, enabling studies of their biology. In addition, this high-throughput technology has been extended to therapeutics, as it provides an alternative functional platform for rapid drug screening and disease modelling. Functional tissue models fabricated using 3D bioprinting mimic native tissues and help in the development of platforms for personalized drug screening and disease modelling due to their high throughput and ease of customization. Moreover, bioprinted 3D tissue models mimic native tissues more closely and provide added advantages over earlier conventional tissue models, such as monoculture, co-culture, explants, etc. In this context, this review article provides an overview of different bioprinted in vitro tissue models of skin, bone, neural tissue, vascular tissue, cartilage, liver and cardiac tissue. This article explores advancements and innovations in these models in terms of developing improved therapeutic interventions. Herein, we provide an insight into the development of different bioprinted tissue models for applications in drug screening and disease modelling. The needs and advantages of bioprinted tissue models as compared with conventional in vitro models are discussed. Furthermore, the different biomaterials, cell sources and bioprinting techniques used to develop tissue models are briefly reviewed. Thereafter, different bioprinted tissue models, namely skin, liver, vascular, cardiac, cartilage, bone and neural tissue, are discussed in detail with a special emphasis on drug screening and disease modelling. Finally, challenges and future prospects are highlighted and discussed. Taken together, this review highlights the different approaches and strategies used for the development of different 3D bioprinted in vitro tissue models for improved therapeutic interventions.
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